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Mastering Probabilistic Graphical Models with Python

You're reading from  Mastering Probabilistic Graphical Models with Python

Product type Book
Published in Aug 2015
Publisher
ISBN-13 9781784394684
Pages 284 pages
Edition 1st Edition
Languages
Author (1):
Ankur Ankan Ankur Ankan
Profile icon Ankur Ankan
Toc

Table of Contents (14) Chapters close

Mastering Probabilistic Graphical Models Using Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Bayesian Network Fundamentals 2. Markov Network Fundamentals 3. Inference – Asking Questions to Models 4. Approximate Inference 5. Model Learning – Parameter Estimation in Bayesian Networks 6. Model Learning – Parameter Estimation in Markov Networks 7. Specialized Models Index

Collapsed particles


So far, we have discussed various techniques to sample using full instantiations over the variables. However, the problem with full instantiations is that they can only cover a very small region of the space, as the space is exponential to the number of variables. The solution to this is to have partial instantiations of the variables and use a closed-form representation of a distribution over the rest. Collapsed particles are also known as Rao-Blackwellized particles.

So, considering as the set of variables over which we will do the assignments and which the particle will depend on, and as the set of variables over which we define a closed-form distribution, if we want to estimate the expectation of some function relative to our posterior distribution we have the following:

Also, we are assuming that the internal expectation can be computed easily. So essentially, we are using a hybrid approach in the case of collapsed particles. We generate particles for the variables...

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